Amazon (AWS) SageMaker Data Wrangler | End-to-End ML Project | SageMaker Tutorial | SageMaker Studio

In this tutorial, we’ll walk you through a complete machine learning classification project using the Titanic dataset, powered by Amazon SageMaker Data Wrangler, AWS Data Wrangler, SageMaker Canvas, and SageMaker Studio. From data analysis and transformation to model building, training, and deployment—everything is covered step-by-step, with no coding required. Whether you’re new to SageMaker or exploring how it streamlines ML workflows, this video will show you exactly how to build and deploy an ML project with ease.

🔍 Here’s what you’ll learn:
✅ Import the sample Titanic dataset from Amazon S3
✅ Explore data insights—missing values, duplicates, feature importance
✅ Clean and transform data using the powerful Generative AI in Canvas
✅ Build a classification model
✅ Run batch predictions on unseen data
✅ Deploy Model and Test Using Model Endpoint
✅ Call Model Endpoint Using Python code and AWS CLI
✅ Export results to CSV to compare the prediction result
✅ Delete the AWS Resources
✅ General pricing about data wrangling, building, and deploying model on SageMaker

Dataset: https://github.com/KnoDAX/AWS-AI-ML/t...
Endpoint Invoking Code: https://github.com/KnoDAX/AWS-AI-ML/t...

🎯 Perfect for: Data scientists, ML engineers, and anyone interested in no-code machine learning with AWS.

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